As artificial intelligence becomes increasingly important in enterprise software development, Java developers need practical ways to integrate AI capabilities into their applications. Spring AI provides a familiar and pragmatic approach to this challenge, allowing developers to incorporate AI features while leveraging the established patterns and practices of the Spring ecosystem.

This session demonstrates Spring AI's portable APIs that enable developers to switch seamlessly between different AI models and vector stores. We'll show how this abstraction layer allows you to write code once and run it with providers like OpenAI, Azure OpenAI, or local models, as well as vector stores such as Postgres, Chroma, or Weaviate.

Through live coding demonstrations, we'll showcase:
* RAG (Retrieval-Augmented Generation) implementation using vector databases for intelligent document processing
* Function Calling patterns that enable AI models to interact with your business logic
* Evaluation techniques to measure LLM response accuracy and mitigate hallucination
* Observability features to monitor your AI application's behavior in production